Systems and methods for seizure detection based on changes in electroencephalogram (eeg) non-linearities

ABSTRACT

A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to determine metrics based on the EEG signal, the metrics indicating non-linear features of the EEG signal, determine that the EEG signal indicates a candidate seizure by determining, based at least in part on the metrics, a change in the non-linear features of the EEG signal over time, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure. The change in the non-linear features indicates a physiological force that gives rise to the candidate seizure.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application is a continuation of P.C.T. Application No.PCT/US2020/025136 filed Mar. 27, 2020 which claims the benefit of andpriority to U.S. Provisional Patent Application No. 62/890,497 filedAug. 22, 2019, the entirety of each of which is incorporated byreference herein.

BACKGROUND

The present disclosure relates generally to electroencephalogram (EEG)analysis. More particularly, the present disclosure relates to EEGanalysis for seizure detection in a patient.

Seizures occur commonly in patients with a wide range of medical issues.Seizures afflict more than fifty million persons worldwide. In somecases, seizures may be benign but in an extreme form, a seizure can belife threatening. Accordingly, it is important to detect and respond toseizures. The earlier seizures are detected and treated, the better theoutcome for the patient. However, detecting a seizure may be challengingsince there may be no visible signs that a seizure is occurring in apatient. In particular, it may be difficult to visually detect that apatient in intensive care or a young patient (a child or infant) areexperiencing a seizure.

Accordingly, often, a record of EEG data may be collected for such apatient for analysis by an epileptologist, in some cases, up totwenty-four hours of continuous EEG data recording may be necessary formanual analysis by the epileptologist. Manual analysis of such a largeamount of data may be cumbersome, time consuming, and expensive. SomeEEG analytics algorithms for seizure detection exist, however, thesealgorithms have low performance in young children. For example, someseizure detection algorithms may reach detection rates of 80% in adultsbut only attain detection rates of 50-60% in young children.Furthermore, such algorithms may also have a large number of falsepositive rates, in some cases, more than 100 false positives per day fora single patient when the patient is a young child. This number of falsepositives requires manual review for all records of a child and as theanalysis algorithms do not appropriately reduce the amount of manual EEGdata review required. Part of the failure of such EEG detectionalgorithms in children is due to the high variability in the nature ofthe abnormal EEG waveforms recorded for children.

SUMMARY

One implementation of the present disclosure is a seizure detectionsystem including one or more circuits. The one or more circuits areconfigured to receive an electroencephalogram (EEG) signal generatedbased on electrical brain activity of a patient. The one or morecircuits are configured to determine metrics based on the EEG signal.The metrics indicate non-linear features of the EEG signal. The one ormore circuits are configured to determine that the EEG signal indicatesa candidate seizure by determining, based at least in part on themetrics, a change in the non-linear features of the EEG signal overtime, and generate a seizure alert indicating that the EEG signal mayindicate a candidate seizure. A “candidate seizure,” as used herein, mayrefer to any seizure, epileptic discharge, sub-clinical event, potentialseizure for technician review, or the like. The change in the non-linearfeatures indicates a physiological force that gives rise to thecandidate seizure. The time between an occurrence of the change in thenon-linear features and an occurrence of the candidate seizure may vary.

In some embodiments, the processing circuit is configured to determinethat the EEG signal indicates the candidate seizure based on at leastone of a default parameter value or a user defined parameter value.

In some embodiments, the seizure detection system is a cloud-basedsystem, wherein the one or more circuits are configured to receive theEEG signal from a local EEG acquisition system via a network and provideresult data to the local EEG acquisition system via the network.

In some embodiments, the seizure detection system is a local system. Insome embodiments, the local system is integrated with a local EEG systemor connected locally to an EEG acquisition system.

In some embodiments, determining, based at least in part on the metrics,the change in the non-linear features of the EEG signal includesdetermining an increase in the non-linear features over time. In someembodiments, the method includes determining an overall change of thenon-linear features (e.g., values indicating how much the non-linearfeatures have changed). In some embodiments, the method includesdetermining an overall increase or overall decrease of the non-linearfeatures (e.g., in each of the non-linear features) over a time period.In some embodiments, the method includes determining whether atrajectory of the non-linear features increases or decreases over thetime period.

In some embodiments, the metrics include at least one of dimensionality,synchrony, Lyapunov exponents, entropy, global non-linearity, distancedifferences between recurrence trajectories, self-similarity, oreigenvalues.

In some embodiments, the one or more circuits are configured todetermine that the EEG signal indicates a seizure alert by determining,based at least in part on the metrics, the change in the non-linearfeatures of the EEG signal over time by performing a preliminaryanalysis with one of the metrics, wherein the preliminary analysisindicates that the EEG signal indicates the candidate seizure or thatthe EEG signal includes noise and performing a secondary analysis withone or more metrics of the metrics to determine whether the EEG signalindicates the candidate seizure or that the EEG signal includes thenoise and/or other artifacts.

In some embodiments, the one or more circuits are configured todetermine probabilities of a trajectory of each of the plurality ofmetrics at a plurality of points in time and determine whether thetrajectory of each of the plurality of metrics is significant based onthe probabilities. In some embodiments, determining, based at least inpart on the plurality of metrics, the change in the non-linear featuresof the EEG signal includes mapping significant metrics of the pluralityof metrics to a category, wherein the category results in a seizurealert. In some embodiments, determining the change in the non-linearfeatures may include determining a trajectory of the non-linearfeatures. A trajectory may be a pattern of change in a metric over time.The trajectory can be determined for a metric by plotting values (orrecording values) of the metric. In some embodiments, determining thetrajectory includes plotting values of the metric in phase space anddetermining the trajectory of the metric from the phase space plot.

In some embodiments, the one or more circuits are configured todetermine a dimensionality of the EEG signal by performing a phase spaceanalysis by increasing a value of the dimensionality until a number offalse neighbors reaches zero, wherein a starting value of thedimensionality is based on an age of the patient.

In some embodiments, the one or more circuits are configured todetermine whether one or more of the metrics exhibit statisticallysignificant changes over time and generate a user interface. In someembodiments, the user interface includes a real-time trend of the EEGsignal and the one or more of the metrics. In some embodiments, the oneor more circuits are configured to cause a user interface device todisplay the user interface.

In some embodiments, one of the metrics is an eigenvalue, wherein theuser interface further includes a trend of the eigenvalue. In someembodiments, the eigenvalue can be plotted along with an EEG signal suchthat the user interface can provide an operator with a view of the EEGsignal and also the trend of the eigenvalues.

In some embodiments, the user interface further includes a historicalwindow of the EEG signal, the historical window of the EEG signalassociated with the candidate seizure.

In some embodiments, the one or more circuits are configured todetermine a moving window of eigenvalues of the EEG signal, determinethat the eigenvalues are decreasing, and determine that the EEG signalis consistent with a candidate seizure based at least in part on themetrics in response to a determination that the eigenvalues aredecreasing.

In some embodiments, the one or more circuits are configured todetermine Renyi permutation entropy values (and/or any other types ofentropy measures) based on the EEG signal, determine that the Renyipermutation entropy values are decreasing, and determine that the EEGsignal indicates the candidate seizure in response to the determinationthat the eigenvalues are decreasing and a second determination that theRenyi permutation entropy values are decreasing.

In some embodiments, the one or more circuits are configured todetermine Renyi permutation entropy values based on the EEG signal,determine that the Renyi permutation entropy values are increasing,determine sample entropy values based on the EEG signal in response to afirst determination that the Renyi permutation entropy values areincreasing, determine that the EEG signal indicates the candidateseizure in response to a second determination that the sample entropyvalues are decreasing (e.g., negative), and determine that the EEGsignal does not indicate the candidate seizure in response to a thirddetermination that the sample entropy values are increasing (e.g.,positive).

Another implementation of the present disclosure is a method of seizuredetection. The method includes receiving, by a processing circuit, anelectroencephalogram (EEG) signal generated based on electrical brainactivity of a patient, determining, by the processing circuit, metricsbased on the EEG signal, the metrics indicating non-linear features ofthe EEG signal, determining, by the processing circuit, that the EEGsignal indicates a candidate seizure by determining, based at least inpart on the metrics, a change in the non-linear features of the EEGsignal (e.g., in each of the non-linear features) over time, andgenerating, by the processing circuit, a seizure alert indicating thatthe EEG signal indicates the candidate seizure. The change in thenon-linear features reflects a physiological force that gives rise tothe candidate seizure.

In some embodiments, determining, by the processing circuit, that theEEG signal indicates the candidate seizure is based on at least one of adefault parameter value or a user defined parameter value.

In some embodiments, the metrics include at least one of dimensionality,synchrony, Lyapunov exponents, entropy, global non-linearity, distancedifferences between recurrence trajectories, self-similarity, oreigenvalues.

In some embodiments, determining, by the processing circuit, that theEEG signal indicates the candidate seizure by determining, based atleast in part on the metrics, the change in the non-linear features ofthe EEG signal over time by performing a preliminary analysis with oneof the metrics, wherein the preliminary analysis indicates that the EEGsignal indicates the candidate seizure or that the EEG signal includesnoise and performing a secondary analysis with one or more metrics ofthe metrics to determine whether the EEG signal indicates the candidateseizure or that the EEG signal includes the noise.

In some embodiments, the method further includes determining, by theprocessing circuit, probabilities of a trajectory of each of theplurality of metrics at a plurality of points in time and determining,by the processing circuit, whether the trajectory of each of theplurality of metrics is significant based on the probabilities. In someembodiments, determining, by the processing circuit based at least inpart on the plurality of metrics, the change in the non-linear features(e.g., a change in the trajectory of the non-linear features) of the EEGsignal includes mapping significant metrics of the plurality of metricsto a category, wherein the category is a seizure category.

In some embodiments, the method includes determining, by the processingcircuit, a dimensionality of the EEG signal by performing a phase spaceanalysis by increasing a value of the dimensionality until a number offalse neighbors reaches zero, wherein a starting value of thedimensionality is based on an age of the patient.

Another implementation of the present disclosure is a seizure detectionsystem including one or more electrodes connected to a patient, theelectrodes configured to generate an electroencephalogram (EEG) signalbased on electrical brain activity of a patient. The system furtherincludes a processing circuit configured to receive anelectroencephalogram (EEG) signal generated based on electrical brainactivity of a patient, determine metrics based on the EEG signal, themetrics indicating non-linear features of the EEG signal, determine thatthe EEG signal indicates a candidate seizure by determining, based atleast in part on the metrics, a change in the non-linear features of theEEG signal (e.g., in each of the non-linear features) over time, andgenerate a seizure alert indicating that the EEG signal indicates thecandidate seizure. The change in the non-linear features indicates aphysiological force that gives rise to the candidate seizure.

In some embodiments, the processing circuit is configured to determinethat the EEG signal indicates the candidate seizure based on at leastone of a default parameter value or a user defined parameter value.

In some embodiments, the processing circuit is configured to determinethat the EEG signal indicates the candidate seizure by determining atrajectory of a non-linear feature of the non-linear features bydetermining values of the non-linear feature over time, determining thata probability value of the trajectory is less than a probability level(e.g., a predefined critical probability level set by a categorizationcriteria) indicating that the occurrence of the trajectory isstatistically significant, and determining that the EEG signal indicatesthe candidate seizure in response to a determination that theprobability value of the trajectory is less than the probability level.In some embodiments, probability levels of the trajectories of multiplenon-linear features are each compared to the probability level todetermine that the trajectories are each statistically significantand/or whether the signal indicates the candidate seizure.

In some embodiments, each value of the values of the non-linear featuredecreases with respect to a previous value of the values (i.e., eachindividual value of a pattern of values of the non-linear featuredecreases relative to a prior value). In some embodiments, theprocessing circuit is configured to determine the probability value ofthe trajectory based on a number of the values each decreasing withrespect to the previous value of the values (i.e., the number ofconsecutively decreasing values).

In some embodiments, the processing circuit is configured to receiveuser input via a user interface and determine the probability level bysetting the probability level to a value selected by the user via theuser input.

In some embodiments, the processing circuit is configured to retrieve adefault value from a memory device of the seizure detection system anddetermine the probability level by setting the probability level to thedefault value retrieved from the memory device.

In some embodiments, the processing circuit is configured to determinethat the EEG signal indicates the candidate seizure by determining atrajectory of each of the non-linear features by determining values ofeach of the non-linear features over time, determining that aprobability value of the trajectory of each of the non-linear featuresis less than a probability level indicating that the occurrence of thetrajectory of each of the non-linear features is statisticallysignificant, and determining that the EEG signal indicates the candidateseizure in response to a determination that the probability value of thetrajectory of each of the non-linear features is less than theprobability level.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objects, aspects, features, and advantages of the disclosurewill become more apparent and better understood by referring to thedetailed description taken in conjunction with the accompanyingdrawings, in which like reference characters identify correspondingelements throughout. In the drawings, like reference numbers generallyindicate identical, functionally similar, and/or structurally similarelements.

FIG. 1 is a block diagram of a local EEG system including a seizuredetector for candidate seizure detection based on trends of non-linearfeatures in an EEG signal, according to an exemplary embodiment.

FIG. 2 is a block diagram of an EEG acquisition system for collectingEEG data and an analysis system including the seizure detector foranalyzing the EEG data to detect a candidate seizure, according to anexemplary embodiment.

FIG. 3 is a block diagram of a remote cloud based system including theseizure detector for candidate seizure detection, according to anexemplary embodiment.

FIG. 4 is a block diagram of the seizure detector of FIGS. 1-3 shown ingreater detail, according to an exemplary embodiment.

FIG. 5 is a flow diagram of a process of detecting a candidate seizureby determining changes of non-linear features of an EEG signal that canbe performed by the seizure detector of FIG. 4, according to anexemplary embodiment.

FIG. 6 is a flow diagram of a process of detecting a candidate seizureby determining changes of non-linear features of an EEG signal witheigenvalues, Renyi permutation entropy, and sample entropy that can beperformed by the seizure detector of FIG. 4, according to an exemplaryembodiment.

DETAILED DESCRIPTION Overview

Referring generally to the FIGURES, systems and methods for seizuredetection based on changes in EEG non-linearities are shown, accordingto various exemplary embodiments. A seizure detector is configured todetect whether a candidate seizure is present in EEG data by analyzingchanges in the non-linearities overtime, i.e., by detecting a change innon-linearities in the EEG data overtime (for example an increase), insome embodiments. The seizure detector is configured to analyze an EEGsignal to determine whether there are epochs or events in the EEG signalwhich can be surfaced to a epileptologist or other clinical technicianfor manual review to definitively determine whether a candidate seizurehas occurred, in some embodiments. The seizure detector can result inmajor time savings in the evaluation of patients undergoing EEGmonitoring for seizure detection.

Rather than trying to detect the abnormal waveforms, e.g., a diverse setof morphologies of spikes and sharp waves in EEG data, the seizuredetector can detect the physiologic forces which give rise to theseabnormal waveforms. The seizure detector, configured to detect candidateseizures through non-linearities in some embodiments, out performsalgorithms focused on a direct detection of abnormal waveform patterns,particularly when the patient is a child or an infant. In children andinfants, the abnormal waveforms may be so varied and diverse, it may bedifficult for the seizure detector to store an indication of eachabnormal waveform. Rather than detecting particular abnormal waveforms,the seizure detector can detect the physiologic forces causing acandidate seizure by monitoring trends in the non-linearity in the EEGsignal.

The seizure detector can utilize physiological or mathematical model(s)of the seizure process. Seizures arise from the abnormal interactionsbetween groups of neurons (i.e., non-linear behavior). Theseinteractions are in contrast to the apparent additivity or linearinteractions present in many non-seizure states. Further, theseinteractions are dynamic in the sense that they evolve during theseizure. Hence, the development of a seizure reflects, or is driven by,an abnormal non-linear transformation between groups of nerve cells,which evolves with the evolution of the seizure. The branch of mathwhich deals with this type of phenomena is called non-linear dynamicsystems analysis.

In some embodiments, the seizure detection performed by the seizuredetector can be utilized by (or integrated with) another system tooperate closed loop stimulation devices dedicated to aborting seizuresin a patient. Furthermore, in some embodiments, the seizure detector canbe utilized to screen long-term historical records of patient EEG data.This can provide more rapid feedback to patients and caregivers thatresults in earlier interventions in seizures. Furthermore, the seizuredetector can be utilized where trained epileptologists are not availablefor manual review of EEG signal data. The shortage of neurologist toreview EEG signals is high within the United States and even higheroutside the United States. It is estimated that nearly 200 pediatricintensive care units (ICUs) and 800 neonatal ICUs in the United Statesdo not have adequately trained personnel to read EEG data.

Dependence upon visual cues (e.g., convulsion, changes in muscle tone,etc.) that a patient is seizing is fraught with problems. Observers(e.g., family members, nurses, technicians, etc.) often fail to detect aseizure in a patient through visible cues, often by as many as 30 to 40%of seizures are missed by observers. In part, this occurs since manyseizures are non-convulsive and therefore more difficult to visuallydetect. Detecting a seizure through a visible cue is particulardifficult in settings such as intensive care units where the ability ofpatients to communicate non-convulsive events (e.g., confusion, aphasia,visual, or other sensory disturbances is compromised) or where patientsmay be too young to communicate these events or may be paralyzed in thecourse of their illness. It is estimated that approximately 30 to 80% ofseizures in an ICU may fall into one of these non-convulsive categories.

Nearly 85% of seizures in adults arise from the temporal lobes andgenerally involves the hippocampus. However, seizures in infants andchildren arise more diffusely from an anatomic perspective and theirtemporal morphology is much more diverse as compared to in adults.Hence, methodologies applied to adults, i.e., morphology based analysis,may not necessarily apply to children. Examples of morphology basedanalysis may include analysis of periodicity present in the EEG data.Often, as a seizure progresses, there is increasing amplitude of the EEGso analysis systems may analyze overall amplitude. Analysis ofperiodicity or amplitude indicate a build-up to a seizure. Otheranalysis algorithms may be linear time-domain analysis based on machinelearning of a particular seizure pattern in a patient. A partial listingof these methods includes independent component analysis, morphologicalanalyses, template matching, mimetic methods, parametric approaches,clustering techniques, and knowledge-based rules. However, such analysismay not be significantly successful in children. Instead, the non-lineartrend analysis of the seizure detector described herein may be appliedto children. Furthermore, such a non-linear trend analysis can also beapplied to adults.

There is little uniformity in the clinical appearance of seizures inchildren. Some are without visible correlates, some are dramatic inappearance (e.g., drop attacks), while some include multiple behavioralcomponents (e.g., Lennox-Gastaut Syndrome). Similarly, there are diverseelectrographic patterns or features which are seen in pediatricseizures. There are the typical patterns of rhythmic build-up seen inadults, periods of voltage suppression (e.g., infantile spasms), “stopand start” patterns, high amplitude rhythmic slowing, bursts ofpolyspikes, etc. Multiple patterns can even be seen in a single patient.The effort to model or template match all of these patterns andtransitions in patterns has not succeeded. Instead of using waveformmorphology as the determinant of the mathematics of detection, theseizure detector described herein is configured to detect the form ofthe underlying physiological forces that produce these diverse changes,in some embodiments.

Furthermore, the same “driving function” can produce diverse outcomesdepending upon the background EEG (reflecting dependence on initialcondition). Since the moment to moment components of the EEG vary soenormously, the result of the action of a driving function on thevariable input is a variable output, in some cases. The system behavioris a joint function of the form of the background activity, the form ofthe physiological force producing the state transition and any externalvariables (e.g. elevated temperature, presence of drugs, metabolicparameters, etc.). Obtaining the form of the driving function is easierduring the brief periods of stationarity while determining its formduring periods of transition is more challenging.

It should be noted that artifacts such as an electromyogram (EMG),movement, patting, electrode “popping” etc., should not have a precedingpattern of trajectory change like that seen in seizures. While themorphology of the observed artifact or other spurious waveforms candemonstrate visual similarity to seizures, these artifacts and otherspurious events should not show the same pattern of physiologicaltrajectory evolution seen in epilepsy. The seizure detector isconfigured to separate spurious events from seizure events to minimizefalse alarms, in some embodiments.

Seizure Detection

Referring now to FIG. 1, a system 100 including an EEG system 104including a seizure detector 108 for candidate seizure detection basedon trends of non-linear features is shown, according to an exemplaryembodiment. In system 100, signal processing firmware and/or softwareare integrated into an EEG data acquisition system with and/or withoutadditional signal processing boards to form the EEG system 104. The EEGsystem 104 is configured to collect EEG data from a patient 102 andfurther detect a candidate seizure (e.g., detect a potential candidateseizure for review by a user) based on the EEG data with the seizuredetector 108, in some embodiments. The seizure detector 108 is a fullyintegrated parallel processor, in some embodiments.

A patient 102 is shown in FIG. 1 with multiple electrodes applied to thehead of the patient 102. The electrodes sense electrical brain activityin the patient 102. The patient 102 may be a human, e.g., an adult, ateenager, a child, an infant, etc. Furthermore, the patient 102 may bean animal, e.g., a cat, a dog, a horse, a cow, etc. The number ofelectrodes applied to the patient 102 for collection of the EEG data foranalysis by the seizure detector 108 may be determined by the desiredprecision of localization (when the focus is detection, accuracy oflocalization is less critical), the dimensions of the driving functiondetermined by the seizure detector 108, the physical limits of the skullsize, the spatial distribution of the electrodes, the spatial extent ofthe source and the correlation structure between the electrodes, etc.

The electrodes are connected to an electrode interface 106 included bythe EEG system 104, in some embodiments. The electrode interface 106 caninclude one or more preliminary hardware circuits for generating the EEGdata for analysis by the seizure detector 108. The hardware circuits mayinclude amplifier circuits (e.g., differential amplifier circuits),filters (e.g., high-pass, low-pass, band-pass), analog to digitalconverters (ADCs), etc.

In some embodiments, the seizure detector 108 can be configured toanalyze signals generated by a full set of electrodes applied to thepatient 102 and/or analyze a subset of electrodes applied to the patient102. In some embodiments, because dimensionality of a seizure does notgenerally exceed a value of four, approximately ten or less electrodescan be analyzed by the seizure detector 108 to detect a seizure. In thisregard, even if a technologist applies a full set of electrodes, theseizure detector 108 can select the appropriate number of electrodesrequired (e.g., select ten electrodes).

In some embodiments, the seizure detector 108 is configured to determinea Lyapunov spectra which generally varies from about two to nine, withmost seizures showing decreasing dimensions with seizure onset. Duringseizures it is unusual to see dimensions above four. Using multichannelEEG methods a trajectory can be characterized with 2d+1 electrodes whered is the estimated dimensionality of the underlying function with theLyapunov spectra. The seizure detector 108 can, during operation,determine the dimensionality of the underlying function and cause userinterface device 110 to recommend a particular number of electrodes forthe patient 102. In this regard, the patient 102 may start with apredefined number of electrodes but, according to the analysis of theseizure detector 108, a technician may add additional electrodes to thepatient 102 based on the determined dimensionality.

The EEG data may be representative of one or multiple EEG signals forbrain activity of the patient 102. The seizure detector 108 can receivethe EEG data and perform a non-linear analysis of the EEG data to detectwhether the EEG data is indicative of a candidate seizure that has,will, or is occurring in the patient 102. The candidate seizuredetections detected by the seizure detector 108 can be provided to theuser interface device 110 for visual and/or audio notification for auser, e.g., a doctor, a nurse, a family member of the patient 102, anepileptologist, a technician, etc. Furthermore, via the user interfacedevice 110, a user may provide configuration data. The configurationdata may indicate the age of the patient 102, the weight of the patient102, historical EEG data of the patient 102, medical conditions of thepatient, etc. The non-linear analysis that the seizure detector 108 isconfigured to perform may be based, at least in part, on theconfiguration data.

The user interface device 110 may be a system or device configured toreceive input from a user and/or provide output to the user. The userinterface device 110 can be a monitor, e.g., a display screen. Thedisplay screen may be a light emitting diode (LED) screen, a Cathode raytube display (CRT), a liquid crystal display (LCD) and/or any other typeof display screen. The user interface device 110 may further includeinput devices, a mouse, a keyboard, a touch-screen, etc. Furthermore,the user interface device 110 may include a speaker for audio output, amicrophone for audio input, etc. In some embodiments, the user interfacedevice 110 is a computer, a smart phone, a tablet etc. in communicationwith the EEG system 104 and/or the seizure detector 108.

In principle, if a specific chain of events that leads to the emergenceof a seizure are known, a system is configured to search for thespecific chain of events, in some embodiments. For example, much isknown about the abnormal electrical behavior of single neurons in thecausative anatomic regions of seizures. For example, particularlytemporal lobe seizures can be recognized in adults. However, seizuresare caused by malfunctioning networks or assemblies of brain cells.Therefore, the seizure detector 108 can analyze a population ofbehaviors in search for driving forces behind seizure onset in thepatient 102 instead of searching for a known morphological pattern(e.g., activity in particular areas of the brain, sharp spikes inactivity, etc.). The forces behind the physiology of a seizure are notrandom. In fact, the forces are deterministic and can be detected by theseizure detector 108 by applying non-linear dynamic systems tools.

The seizure detector 108 is configured to apply seizure detection to anyrange of ages and can be performed in real-time, in some embodiments.Furthermore, the accuracy of the seizure detector 108 may be greaterthan 90% and less than double digit false positives in EEG datacollected for the patient 102 over a 24 hour period. The seizuredetector 108 can be implemented locally (as illustrated in FIGS. 1 and2) and/or can be implemented remotely (as illustrated in FIG. 3).

In some embodiments, the seizure detector 108 is configured to selectparameter values for detecting a seizure and/or categorizing an eventperformed by the seizure detector 108 based on user input (instead of,or in addition to, using default values programmed into the seizuredetector 108). In some embodiments, the parameter values can be selectedmanually by a user, where the user provides user input via the userinterface 110 associated with the seizure detector 108. The parametervalues may be trajectory statistical significance level(s) and/or metricparameter values between component metrics when multiple metrics aresimultaneously applied to a dataset. By selecting the parameter valuesbased on the user input, false alarms generated by the seizure detector108 can be reduced or a hit rate by the seizure detector 108 can beincreased. Furthermore, by allowing a user to select the parametervalues, the appropriate tradeoffs between false positives and truepositives can be achieved by the seizure detector 108.

The user input can indicate a balance level (e.g., a weight) betweendecreasing false positives and increasing hit rates. This can beaccomplished either by use of the suggested default values or byadjustment of the values, where the adjustment can be made based onpersonal preference or to best suit a particular patient situation. Thebalance level can be a value in a range and can correspond to lower orhigher statistical significance levels (e.g., a balance level thatfavors decreasing false positives may be associated with lowerprobability values of a trajectory of a metric changing in a particulardirection (increasing or decreasing) while a balance level that favorsincreasing hit rates may be associated with a higher probability valuefor the trajectory of the metric changing).

The seizure detector 108 is configured to detect shifting patterns offorces which produce the state transition from non-seizure to seizureswithout attempting to detect target waveform morphologies, in someembodiments. These abnormal physiological forces produce waveformtrajectories that the seizure detector 108 is configured to quantify, insome embodiments. The seizure detector 108 can utilize the trajectoriesto detect multiple state changes, including seizure state changes, i.e.,a change from a normal state in the patient 102 into a seizure state.More specifically, the seizure detector 108 is configured to determineone or multiple non-linear metrics based on EEG data which reflect theemergence of these trajectories, in some embodiments. The seizuredetector 108 is configured to apply non-linear dynamic system tools todetect the emergence of these abnormal trajectories, in someembodiments.

The seizure detector 108 is configured to search for a seizure, in anEEG time series, by searching for a specific category of state change,in some embodiments. More particularly, the seizure detector 108 isconfigured to search for a change, i.e., an alteration to the structuralnon-linearities in the EEG data, in some embodiments. The seizuredetector 108 is configured to apply non-linear methods to detecting thestate changes in a mathematical state space, i.e., a starting point forthe reconstruction of the systems dynamics (the dynamics of the brainactivity of the patient 102).

The seizure detector 108 is configured to detect candidate seizureswhere there is a gradual and/or an abrupt transitions into the seizurestate in the patient 102, in some embodiments. More particularly, theseizure detector 108 can apply dynamic systems analysis to detect boththe abrupt changes, (e.g., bifurcations), along with many forms ofgradual change. The search for a seizure is not a search for a specificisolated event nor a specific single value of a feature, instead, theseizure detector 108 can determine multiple non-linear metrics and trackthe non-linear metrics overtime to detect diagnostic shifts and patternsof changes in non-linear features of the EEG data. For example, theseizure detector 108 can determine whether a statistically significantincreasing or decreasing trajectory of the non-linear features isoccurring. For example, for a metric indicating non-linearity, sevensequential increasing values for the metric may be statisticallysignificant to indicate that the trajectory is increasing. However, fivesequential increases in the value of the metric may not be statisticallysignificant to indicate an increasing trajectory. Similarly, the numberof sequential decreasing values can be associated with a probability ofoccurring, e.g., five sequential decreasing values of the metric may notbe significant while seven sequential decreasing values of the metricmay be statistically significant.

For example, the probability that five sequential increasing values of ametric may be 0.032 (which can be determined by the seizure detector 108from the five sequential increasing values and/or historical trajectorydata). The probability that seven sequential increasing values of ametric may be 0.01. Because the probability that seven sequentialincreasing values is less than the probability that five sequentialincreasing values, seven sequential increasing values may be a greaterstatistical significance than five sequential increasing values (a lowerprobability level). A probability threshold could be applied by theseizure detector 108 to determine whether the increase or decrease of ametric is statistically significant, e.g., is the probability of theoccurrence less than the probability threshold. Furthermore, the seizuredetector 108 could apply a change number threshold, i.e., is the numberof sequential increasing or sequential decreasing values greater than orequal to the change number threshold, i.e., if the threshold is seven,seven sequential increasing values of a metric are statisticallysignificant while five sequential increasing values of the metric arenot statistically significant.

The threshold for determining statistical significance can define theamount of false positives and missed seizure detections. For example, athreshold that requires a higher number of sequential increasing orsequential decreasing values may have less false positives but miss ahigh number of seizures. However, a lower value of the threshold mayresult in more false positives but miss less seizures. An optimizationcan be performed by the seizure detector 108 to properly set thethresholds for determining statistical significance. The optimizationmay attempt to minimize missing seizures and minimize false positives.The optimization can be based on user input, e.g., user feedback thatidentifies certain periods of a historical EEG signal as correspondingto a seizure or other periods of the EEG signal pertaining to a falsepositive.

The seizure detector 108 is configured to detect a change in the patternof non-linear dynamics of the EEG data since the pattern of change is aconstant aspect of the seizure state transition, in some embodiments.Often, the state changes in non-linearities precede, in time, theappearance of spikes, sharp waves or other visual signs in the EEG dataof an electrographic or clinical seizure. Hence, the seizure detector108 is configured to first determine a non-specific detector of changesin non-linearities from the EEG, i.e., eigenvalues, in some embodiments.If the non-specific detector indicates a candidate seizure, the seizuredetector 108 can apply subsequent metric calculation and/or analysis.This allows the seizure detector 108 to save computational resources byapplying low computational requirement calculation, e.g., eigenvalues,followed by higher computational requirement calculations, e.g.,dimensionality.

Because of the potential instability of multiple non-linear measures, atsmall sample sizes, the seizure detector 108 is configured to apply amoving window for calculations of the metrics, in some embodiments. Theparticular values of the moving window duration and percent overlapwithin the window, may be predefined based on the specific metric, i.e.,each metric may be associated with its own window duration and percentoverlap. The greater the dependence of the particular metric upon samplesize, to ensure stability of estimates, the seizure detector 108 isconfigured to determine the metric with a longer the window duration, insome embodiments.

The seizure detector 108 is configured to analyze changes in eigenvaluesto detect a seizure, in some embodiments. However, changes ineigenvalues can arise from either quantitative changes in the ratio oflinear to non-linear activity of the EEG data, or the presence of noisewithin the EEG data. Hence seizure detector 108 can determine andanalyze multiple non-linear metrics together to detect a candidateseizure. For example, the seizure detector 108 is configured todetermine entropy along with the eigenvalues to help make thisdistinction between a seizure and noise, in some embodiments. Noiseoften increases entropy, when the noise is not rhythmic, while mostseizures decrease entropy.

The seizure detector 108 is configured to determine and analyze manyother non-linear metrics, in some embodiments. The metrics that theseizure detector 108 is configured to analyze may be based on theconfiguration data, i.e., a clinical picture or syndrome of the patient102 (e.g. drop attacks, infantile spasms, Lennox Gastaut Syndrome, posthypoxic encephalopathy, age, weight, etc.) and a baseline EEG patternassociated with the patient 102. The seizure detector 108 is configuredto analyze the particular configuration data and determine and/oranalyze the metrics appropriate for the patient 102, in someembodiments.

The clinical syndromes and the baseline EEG pattern (e.g., EEG patternsof normal brain activity, seizure patterns, etc.), the age of thepatient, the weight of the patient, etc. can be included in theconfiguration data and can be utilized by the seizure detector 108 inthe selection of the composition of the mixture (or a weighting of themixture) of non-linear metrics in the second phase (and/or thepreliminary phase). Candidate metrics include but are not limited todimensionality, synchrony, Lyapunov exponents, various forms of entropy,global nonlinearity (via surrogate testing), distance differencesbetween the recurrence trajectories in phase space, self-similarity,etc.

The output of the analysis performed by the seizure detector 108 may bea panel of non-linear values that change over time. Some of thesepatterns may be indicative of candidate seizures while other patternsreflect sleep onset and others, artifacts. Accordingly, the seizuredetector 108 can map the panel of non-linear values to particularcategories, e.g., seizure, noise, sleep, etc. The number of metrics inthe panel may be set by the seizure detector 108 based on by the signalprocessing power of the hardware and/or firmware architecture of the EEGsystem. The selection of the metrics may change based on whether theseizure detector 108 is operating in a real-time mode where EEG data isbeing analyzed in real-time or in a historical analysis mode wherepreviously recorded EEG data is analyzed.

Referring now to FIG. 2, a system 200 including an EEG acquisitionsystem 206 for collecting EEG data and an analysis system 204 includingthe seizure detector 108 for analyzing the EEG data to detect acandidate seizure is shown, according to an exemplary embodiment. In thesystem 200, the signal processing hardware, firmware, and/or software ofthe seizure detector 108 is fully integrated into a stand-alone localcomputer separate from the EEG acquisition system 206, i.e., in theanalysis system 204.

The analysis system 204 is configured to operate with the EEGacquisition system 206 using the output of the EEG acquisition system,i.e., the EEG data acquired by the EEG acquisition system 206, in someembodiments. The system 200 can be implemented in multiple embodiments,e.g., the analysis system 204 can be a screening device with asimplified head-box for the EEG acquisition system 206 and limitedsignal processing capabilities. The head-box could be structured to siton top of an enclosure of the EEG acquisition system 206. The system 200may be appropriate for warning and/or screening at a hospital or withina home of a patient. In some embodiments, the analysis system 204 is aplugin card (e.g., a circuit board configured with a connection portthat can connect to a connection port of the EEG acquisition system206). A user can insert the plugin card into the EEG acquisition system206 to give the EEG acquisition system 206 all of the operationalabilities of the analysis system 204. For example, the plug-in card caninclude a graphics or digital signal processing circuit and memorycomprising instructions for implementing the operations describedherein.

The EEG acquisition system 206 may include an acquisition manager 202.The acquisition manager 202 is configured to collect the EEG data andmaintain a historical record of the EEG data. Furthermore, the EEGacquisition manager 202 can provide the EEG data to the analysis system204 for analysis and seizure detection. Upon receiving a request fromthe analysis system 204, the acquisition manager 202 can provide theanalysis system 204 requested historical EEG data that the acquisitionmanager 202 stores.

Referring now to FIG. 3, a system 300, a cloud-based implementation ofthe seizure detector 108 is shown, according to an exemplary embodiment.In the system 300, the seizure detection and associated signalprocessing is performed at a remote site, i.e., by a cloud platform 306.The cloud platform 306 may be one or more remote servers and/or localservers within a hospital, can be a cloud analysis system such asMICROSOFT AZURE, AMAZON WEB SERVICES, etc.

The EEG system 100 includes a network interface 302 which communicatesthe EEG data and/or the configuration data to the cloud platform 306 foranalysis by the seizure detector 108 via a network 304. The network 304can act as a pipeline between the EEG system 100 and the cloud platform306 where the feature extraction and/or analysis is performed by theseizure detector 108. Results of the analysis performed by the analysissystem 204 can be transmitted back to the EEG system 100 for display viathe user interface device 110 and decision making by a user.

The network 304 can include one or multiple different wired and/orwireless networks. The networks may be a local area network (LAN) or awide area network (WAN). The networks may be wired and include Ethernetwires, cables, and/or fiber optic connections and/or may be wireless andbe Wi-Fi and/or cellular based networks. The network interface 302 caninclude one or more receivers, transmitters, transceivers, wirelessradios, signal processing circuitry, etc. that the network interface 302is configured to operate to communicate via the network 304, in someembodiments.

Referring now to FIG. 4, a system 400 including the seizure detector 108is shown, according to an exemplary embodiment. The seizure detector 108is shown to receive the configuration data and the EEG data.Furthermore, the seizure detector 108 is shown to output a userinterface causing the user interface device 110 to display the userinterface. The user interface may include indications of the presence ofa candidate seizure and/or calculated metrics that the seizure detector108 determines from the EEG data.

The seizure detector 108 includes an analysis circuit 428. The analysiscircuit 428 can include one or more processing circuits for digitalsignal processing. The analysis circuit 428 can include fieldprogrammable gate arrays (FPGAs), application specific integratedcircuits (ASICs), one or more central processing units (CPUs), one ormore digital signal processing (DSP) units,_one or more graphicsprocessing units (GPUs), etc. There may be high processing requirementsof the seizure detector 108 and the seizure detector 108 can applyshared computing across multiple processing units (e.g., separateprocessing cards, graphics cards, remote servers, cloud-based systems,etc.).

Furthermore, the analysis circuit 428 can include one or more memorydevices. The memory devices can store instructions and/or computed datafor execution on one or more processors. The memory devices can includerandom access memory (RAM), solid state drives (SSDs), hard disk drives(HDDs), FLASH memory, electrically erasable programmable read-onlymemory (EEPROM), and/or any other type of memory, either transitory ornon-transitory.

The seizure detector 108 is configured to detect candidate seizures withan analysis of historical data and/or in a real-time analysis, in someembodiments. The seizure detector 108 is configured to detect acandidate seizure with less than fifteen seconds of delay betweenseizure onset and detection, in some embodiments. The seizure detector108 is configured to detect a wide range of electrographic patterns(i.e., a mapping between types of seizures and an optimal detectionalgorithm performed by the seizure detector 108 for that type ofseizure), in some embodiments. Furthermore, the seizure detector 108 isconfigured to separate seizure state transitions from artifacts andnoise, in some embodiments. Furthermore, the seizure detector 108 isconfigured to detect several seizure types within the same patient,arising from several locations, again, within the same patient(multifocality), in some embodiments. The seizure detector 108 may havea true positive rate of more than 90% and a false positive rate of lessthan 8 false detections per day for a single patient.

A constant quantitative feature of the transition from the non-seizureto seizure state is a change in the contribution of non-linearities tothe energy level of the signal. Many, and in adults most, transitions tothe seizure state result in increased rhythmicity or increasedsynchronization between cellular groups. This is reflected in decreasedeigenvalues (decreased contribution of linearities), decreased entropy,decreased dimensionality, and increased global nonlinearity, as revealedby surrogate testing. This pattern is not universal, however. Theexceptions to this pattern are particularly notable in children andinfants where existing algorithms fail. For example, in many patientswith drop attacks the electrographic correlate is an initial brief burstof high energy slowing, followed by low voltage desynchronized activity.The temporal pattern of quantitative metrics would be more complex andshow a period of increased entropy, increased eigenvalues and decreasedglobal non-linearities. For this reason, the seizure detector 108focuses on change in metrics rather than absolute values and utilizesmultiple forms of change, to detect candidate seizures. This captures awide range of ictal electrographic morphologies.

A challenge arises when the baseline EEG activity is poorly organized,has excessive slow wave activity, and is punctuated by high voltagesharp waves or spikes (e.g. Lennox-Gastaut Syndrome). In this case thebaseline eigenvalue that the seizure detector 108 is configured todetect could be so low that the emergence of seizures may not bereflected in a drop of eigenvalues (e.g., a floor effect). In thisinstance the analysis with multiple metrics performed by the seizuredetector 108 increases the likelihood of avoiding floor and/or ceilingeffects. All of these changes can be distinguished from the intrusion ofincreased noise or artifacts (noise generally decreases eigenvalues andincreases entropy).

The seizure detector 108 is configured to apply quantitative temporalchange analysis to multiple metrics identifying a pattern of changeacross metrics which leads to the categorization of an event as acandidate seizure (e.g., a review alert), signal noise (no alert), or anuncertain classification (potential seizure alert occurs), in someembodiments. The seizure detector 108 can be adjusted to alter thetrade-offs between true detections, false alarms, and misses byadjusting the significance levels of the probabilities required to berecognized as a significant change. The detections of the seizuredetector 108 may be performed without utilizing machine learning.Machine learning requires a period of data acquisition with delayedtherapy which may cause damage to a patient. In some embodiments, thetemporal pattern of the metric trajectories can be subjected topost-processing (e.g., smoothing to remove transients) to decrease thevariability in the application of the statistical criteria.

The analysis circuit 428 can apply a pipeline of analysis stages and caninclude a component configured to apply each stage. The components maybe software modules, circuits, etc. The analysis circuit 428 includes achannel selector 402, a filtering stage 404, a preliminary analyzer 406,a secondary analyzer 408, and an interface generator 410. The EEG datareceived by the seizure detector 108 may first pass through the channelselector 402. The channel selector 402 may control which channels of theEEG data the seizure detector 108 performs analysis on. For example,where multiple electrodes are presents, one or more sets of electrodesmay be appropriate for analysis by the analysis circuit 428.Accordingly, the channel selector 402 can select the appropriate EEGsignal channels and provide the EEG signals of the selected channels tothe filtering stage 404.

The filtering stage 404 can filter the EEG data with one or multiple lowpass, high pass, and/or band-pass filters. The filters may be digitaland/or hardware filters, for example, infinite impulse response (IIR)and/or finite impulse response (FIR) filters. The bandwidth appropriatefor the signal analyzed by the analysis circuit 428 may be specific tothe age of the patient 102. Accordingly, the filtering stage 404 mayreceive configuration data indicating a characteristic of the patient(e.g., age) and is configured to perform filtering based on theconfiguration data, in some embodiments.

The bandwidth that the filtering stage 404 passes may depend not only onthe age of the patient but the metrics determined by the seizuredetector 108 for detection of the candidate seizure. In someembodiments, the filtering stage 404 passes frequencies between 100 and200 Hz. In some embodiments, the band of frequencies passed by thefiltering stage 404 may be a range between 2 to 400 Hz.

The analysis circuit 428 is configured to determine multiple non-linearmetrics and combine patterns of evolution of the multiple non-linearmetrics together to detect a candidate seizure via the preliminaryanalyzer 406 and the secondary analyzer 408, in some embodiments. Thepreliminary analyzer 406 and the secondary analyzer 408 is configured toconcatenate the application of several non-linear metric algorithms in asequence and/or in parallel, in some embodiments. The seizure detector108 can detect the presence of a candidate seizure through a firstscreening for non-specific global non-linear transformations, i.e., themetric 430 which may be eigenvalues. Furthermore, the secondary analyzer408 is configured to process more computational intense metrics whichfocus on more specific types of non-linearities via the secondaryanalyzer 408, in some embodiments.

The preliminary analyzer 406 is configured to perform a screening stageby determining a moving window implementation of eigenvalues, e.g., themetric 430, in some embodiments. The eigenvalues decrease with theemergence of nonlinear interactions (e.g., seizures) or the appearanceof noise. The eigenvalues increase when the EEG data becomes lessrhythmic or periodic. Furthermore, the preliminary analyzer 406 isconfigured to determine whether the changes in the eigenvalues arestatistically significant (e.g., have a significance value greater thana predefined amount or an probability of error less than a predefinedamount) by determining the statistical significance 432 of the trend ofthe metric 430 such that only statistically significant changes in theeigenvalues are analyzed by the preliminary analyzer 406 to determine acandidate seizure, in some embodiments.

In some embodiments, the preliminary analyzer 406 determines thestatistical significance with a moving window, i.e., determines trendsof the metric 430 with a moving window similar to, or the same as, themoving window based trend analysis described with reference to thesecondary analyzer 408. Over a particular window of samples of themetric 430, the preliminary analyzer 406 can determine whether themetric 430 is increasing or decreasing. With multiple windows, thepreliminary analyzer 406 can determine a trend of the metric 430 anddetermine a probability level (the statistical significance 432) of thetrend based on previous windows to increase or decrease over futurewindows.

In response to the preliminary analyzer 406 determining a decrease inthe metric 430 by a statistically significant amount (e.g., theprobability of an increase or decrease being less than a particularprobability), the secondary analyzer 408 can calculate and analyze othermetrics, i.e., the metrics 416-420. For example, the metrics 416-420 mayinclude Renyi permutation entropy. The Renyi permutation may bedetermined by the secondary analyzer 408 on only the samples of the EEGdata that the preliminary analyzer 406 detects statistically significantdecreases in eigenvalues on. Permutation entropies may thecomputationally simplest and robust to noise and artifact. The secondaryanalyzer 408 is configured to further determine statistical significanceof the metrics 416-420, i.e., determine the statistical significances422-426 for the metrics 416-420, in some embodiments.

More specifically, each of the metrics 416-420, Mi, calculated by thesecondary analyzer 408 may be time series of data. Based on the timeseries of the metrics 416-420, the secondary analyzer 408 is configuredto determine statistical significances 422-426, P(Mi) that indicate theprobability for a pattern of shifts of a trajectory of the metric underthe null hypothesis, in some embodiments. Similarly, the statisticalsignificances 422-426 can be time series. The trend analyzer 414 isconfigured to analyze a pattern of significant and non-significantvalues of the metrics 416-420 based on the statistical significances422-426 across time, in some embodiments. A current set of significantmetrics can be analyzed by the trend analyzer 414 as a group or panel ofresults. Each panel can be mapped to a particular category, e.g., aclinical category such as a candidate seizure event, no seizure, anindeterminate state, etc. Furthermore, the panels can map to other typesof spurious events (non-seizures).

The metrics 416-420 may be many and varied, for example, there may bemore than a dozen non-linear metric types described with many variantsof each of these metric types. For example there are at least fourteendifferent forms of, or calculation methods for, entropy. The metrics416-420 can include a loss of complexity metric. Each entropy metric mayhave performance advantages and disadvantages in specific settings(e.g., sample entropy performs better than most in detecting voltagesuppression, Kolmogorov entropy is more vulnerable than multiple formsof permutation entropy which also have low computational complexity,etc.). Fuzzy entropy has an appeal in that class membership is graded sothat the user has better control of the class boundaries. The frequencyof the target events (seizures) can be included in the parameter valuesfor some forms of entropy, for example tsalli entropy. Renyi entropy maybe a better selection in instances in which state changes are frequentor profound (e.g., anesthesia). Information regarding the frequency ofseizures, whether or not anesthesia is present, etc. can be included inthe configuration data and thus the secondary analyzer 408 can determineand analyze an appropriate mixture of non-linear metrics. Examples ofmethodologies for calculating entropy can be found in Liang, Zhenhu, etal. “EEG Entropy Measures in Anesthesia.” Frontiers in ComputationalNeuroscience, vol. 9, 2015, doi:10.3389/fncom.2015.00016, the entiretyof which is incorporated by reference herein.

As described, the metrics 416-420 can be based upon and thereforederived from the EEG signal. One important aspect of the metrics 416-420may be a trajectory over time of each of the metrics 416-420. Theabsolute values of the metrics 416-420 may vary enormously, as afunction of patient age, state, syndrome, concomitant medications, etc.Therefore, the trend analyzer 414 is configured to analyze thetrajectory of metrics 416-420, and not necessarily the absolute valuesof the metrics 416-420, to detect and/or classify candidate seizures.The direction of change in the metrics 416-420 over time caused by acandidate seizure (increase versus decrease) can vary based on patientage and/or the type of candidate seizure. For this reason, the secondaryanalyzer 408 is configured to determine the trajectories of the metrics416-420 such that the trend analyzer 414 can determine, based on thetrajectories, whether any segment of the EEG signal is indicative of acandidate seizure and/or should be surfaced for visual evaluation by anelectroencephalographer.

The metrics 416-420 themselves also vary in terms of their stability andreliability, according to sample size. Sample size can be increased byincreasing sample duration. However, an increased sample duration mayrisk missing a seizure event if the seizure event is shorter than therequisite sample duration. In some embodiments, the secondary analyzer408 is configured to determine the direction of change of the metrics416-420 by using moving windows.

For example, at a sampling rate of 400 Hz, a five second window that thesecondary analyzer 408 can be configured to apply contains 2,000samples. The step size and overlap for each of the windows applied tothe metrics 416-420 by the secondary analyzer 408 can be user definedvia the user interface device 110 and/or predefined. Typical valuesmight be one second step sizes with four out of five samples overlappingbetween windows (i.e., four out of five samples being the same betweentwo window positions for a window as the window moves).

Each window, when analyzed by the secondary analyzer 408, may indicatean increase or a decrease of the value of one of the metrics 416-420 andconstitute the trajectory of the metric over time. The trend analyzer414 may have statistical criteria for reviewing and/or analyzing asegment defined by one of the window positions of a window of one of themetrics 416-420. For example, assuming each sample is independent andbehaves randomly, the probability of n consecutive changes in the samedirection would be ½ to the n^(th) power. In some embodiments, thesecondary analyzer 408 is configured to determine the probabilities422-426 for the patterns (increasing or decreasing) of the metrics416-420. The probabilities may be probabilities that a predefined amountof changes will occur in one of the metrics 416-420 in a particulardirection (e.g., a predefined amount of windows into the future willindicate increasing or decreasing values of the metrics based on thetrajectories of previous windows). The trend analyzer 414 can applythreshold values which, if the probabilities rise above or fall belowthe threshold values, indicates that a particular one of the metrics416-420 is increasing or decreasing at a statistically significantlevel. The trend analyzer 414 can apply one or more user defied and/orpredefined thresholds to determine the statistically significant metrics416-420 and/or map the statistically significant metrics 416-420 to acategory, e.g., a seizure, noise, etc.

The selection of particular methods of calculating metrics performed bythe secondary analyzer 408 may be dependent upon, the frequency ofevents, their spatial extent, the sample size, the dimension, the stateof the patient 102, the seizure syndrome of the patient 102, the signalto noise ratio of the time epoch, all of which can be indicated throughthe configuration data or extracted by the secondary analyzer 408 fromthe EEG signal (e.g., signal to noise ratio). The calculation andmapping of metrics performed by the secondary analyzer 408 can take intosignal and subject factors into account as well as the intrinsiccomputational complexity to determine which features should receiveprioritization. This same process applies to the calculation ofdimensionality, complexity (or loss of complexity), Lyapunov exponents,etc.

The metrics 416-420 and their statistical significances 422-426 can bepassed into the trend analyzer 414 which can detect which trends instatistically significant metrics indicate a candidate seizure, noise,etc. For example, when the trend analyzer 414 detect that the Renyipermutation entropy increases determined by the secondary analyzer 408along with the eigenvalues decreasing, the EEG data is indicative ofnoise or a burst suppression pattern of seizures in which caseadditional metrics should be analyzed. For example one of the metrics416-420 may be sample entropy that the secondary analyzer 408, via phasespace analyzer 412, determines in phase space. The sample entropy may becalculated by the secondary analyzer 408 after the calculation andanalysis of the Renyi permutation entropy and/or may be calculated inparallel with the eigenvalues and/or Renyi permutation entropy.Calculation of the sample entropy may be less than a second delay.

Sample entropy may be more sensitive than permutation entropies to burstsuppression. The trend analyzer 414 can determine whether the sampleentropy is positive or negative and can classify the EEG data associatedwith the decreasing eigenvalues as noise if the sample entropy ispositive. These results of the metrics 416-420 can be combined by thetrend analyzer 414 to categorize the event. When both Renyi permutationentropy and eigenvalues decrease, the trend analyzer 414 can determinethat the EEG data is indicative of a candidate seizure and the secondaryanalyzer 408 may not determine the Sample Entropy.

The phase space analyzer 412 is configured to perform a phase spaceanalysis to determine metrics such as dimensionality, in someembodiments. The phase space analyzer 412 is configured to generate aphase space plot for the EEG signal, in some embodiments. Dynamicalsystems can be represented by a series of differential equations whosesolutions may not exist in closed form. However, the phase spaceanalyzer 412 can identify candidate seizure behavior by generating atrajectory in phase space. At each instance in a time series of the EEGsignal, the phase space analyzer 412 is configured to generate a singlepoint in phase space and a sequence of these points form a trajectorywhose pattern provides insight into the nature of the driving function,i.e., insight into the presence or absence of a seizure, in someembodiments. The trajectories can occupy the entirety of the phase spaceor can converge to a lower dimensional region, called an attractor. Thephase space trajectory of noise never converges. When adjacent pointsbegin close to one another and then diverge, a strange attractor is saidto exist and suggests the presence of chaotic behavior.

The phase space analyzer 412 is configured to perform the Takens methodof time shift to generate a phase space plot based on empirical data ofthe EEG time series, in some embodiments. The Takens method is describedin greater detail in Ba

ar, Erol, et al. “Strange Attractor EEG as Sign of Cognitive Function.”Machinery of the Mind, 1990, pp. 91-114,doi:10.1007/978-1-4757-1083-0_5. The EEG signal may be represented asthe time-series,

x(t _(i)), i=1, . . . ,N,

In some embodiments, from this time-series, the phase space analyzer 412is configured to determine a phase space representation of the EEGsignal with a time delay, td and an embedding dimension, m.

X(t _(i))=[x(t _(i)),x(t _(i) +td),x(t _(i)+2td), . . . ,x(t_(i)+(m−1)td)

The shape of the trajectory in phase space can be strongly influenced bythe choice of the time lag, utilized by the phase space analyzer 412 togenerate the phase space plot. In some embodiments, the time lag is thefirst zero in an autocorrelation function and is determined and thenused by the phase space analyzer 412 to embed the signal in phase space.The phase space analyzer 412 is configured to apply one or multipledifferent methods for estimating the time lag. In some embodiments, thephase space analyzer 412 may determine the lag based on a non-linearmetric that the phase space analyzer 412 is attempting to determine. Insome embodiments, the estimators used by the phase space analyzer 412 todetermine the lag are linear and/or non-linear.

The second value which is selected by the phase space analyzer 412 isthe embedding dimension. If the dimension of the attractor is k, thenthe embedding theorem of Witney states that the embedding dimension mustbe 2k+1. Accordingly, the phase space analyzer 412 is configured toselect the embedding dimension based on a known or determined dimensionof the attractor, in some embodiments.

The phase space analyzer 412 is configured to estimate the dimension forphase space with the Cao method, in some embodiments. The dimensionestimated by the phase space analyzer 412 may be a dimension of anattractor within the phase space. The phase space analyzer 412 isconfigured to start with a low dimension and successively increase thedimension until the number of false neighbors reaches zero, in someembodiments. The dimension reached by the phase space analyzer 412 canbe linked to the presence or absence of a candidate seizure. Forexample, the trend analyzer 414 can determine, whether there is acandidate seizure based on the metrics 416-420 and/or based on thedimensionality determined by the phase space analyzer 412.

In some cases, the value of the dimension may be as low as one during aseizure. Furthermore, the dimension is usually below eight interictally.From a practical perspective, with this ascending method performed bythe phase space analyzer 412, i.e., starting from a low dimension andincreasing the dimension value, there can be a real-time compromise ofperformance based upon the computational burden of the ascending method.To overcome this computational burden, the phase space analyzer 412 canreceive the configuration data which indicates the age of the patient.The phase space analyzer 412 is configured to select a startingdimension value based on the age of the patient to reduce the number ofsteps where the phase space analyzer 412 increments the dimensionalvalue and determines when the number of false neighbors reaches zero, insome embodiments.

The starting dimensional value utilized by the phase space analyzer 412in the ascending method may be lower for young children and greater inolder children. This may be because the younger the age the lower thedimensionality, whether ictal or interictal. The selection of a startingdimension value may only be applied for young children, e.g., when theconfiguration data indicates the patient 102 is less than ten years old.There may be no clear difference in dimensionality between awake versussleep in neonates and dimensionality age adjustments may beinsignificant in older children and adults. The trend analyzer 414 mayanalyze trends in the dimensionality, not necessarily the absolute valueof the dimensionality. For example, if the dimensionality fallsovertime, the trend analyzer 414 can classify the EEG signal asindicating a candidate seizure. In this regard, the consequences ofminor errors in the estimates of absolute values of dimensionality arepartially decreased because classification of events is based uponchanges in metrics, rather than absolute values.

The interface generator 410 is configured to generate an interface fordisplay on the user interface device 110 based on the metrics determinedby the preliminary analyzer 406 and/or the secondary analyzer 408, insome embodiments. Furthermore, the interface generator 410 is configuredto generate the interface based on the presence of a candidate seizureas determined by the trend analyzer 414, in some embodiments.Furthermore, the user interface generated by the interface generator 410may be based on user input, e.g., a request to display particularmetrics, display historical EEG data, etc.

In some embodiments, the interface includes a trend of the EEG data inreal-time. In some embodiments, the trend of the EEG data is displayedconstantly. Furthermore, the interface generated by the interfacegenerator 410 may include a superimposed graph of a trend of theeigenvalues determined by the preliminary analyzer 406 over the trend ofthe EEG. There may be a 750 millisecond delay between the eigenvalue andthe EEG waveform. Every 750 milliseconds, the secondary analyzer 408 isconfigured to determine a new value of each of the metrics 416-420, insome embodiments. These values together form a trajectory for each ofthe metrics 416-420. Assuming that each value can only go up or downcompared to the preceding value, the secondary analyzer 408 isconfigured to calculate the probability of n consecutive changes in thesame direction, in some embodiments. If the secondary analyzer 408detects eight consecutive changes in the same direction, this mayindicate a sufficient probability of change in a particular direction.The secondary analyzer 408 may use six seconds of time to determine theprobability of an increase or a decrease of the metrics 416-420 sinceeach metric is determined over a 750 millisecond period and eight valuesmay be determined in total to detect the increase or decrease. Thepreliminary analyzer 406 can be configured to perform the sameprocessing for the metric 430.

When the changes in the eigenvalues become significant, the interfacegenerator 410 is configured to cause the superimposed eigenvaluewaveform to change color, in some embodiments. The interface generator410 is configured to store the EEG trend time linked with the eigenvaluetrend, in some embodiments. This allows a user to request, via the userinterface device 110, a particular portion of historical EEG data. Inresponse to the request, the interface generator 410 can cause theinterface to display the requested portion of EEG data and thecorresponding eigenvalue trend for that requested portion. In someembodiments, any section of EEG data, and the corresponding metricsdetermined for the section of EEG data, that is classified as acandidate seizure, is highlighted in the user interface generated by theinterface generator 410. This can allow a trained clinician to reviewparticular sections of EEG data that is possibly a candidate seizure andmake a final determination regarding whether the section of data isindicative of a seizure.

In some embodiments, the interface generator 410 causes the interface toinclude a panel of the non-linear metrics determined by the secondaryanalyzer 408 that are statistically significant. The interface generator410 can receive a user specified significance level via the userinterface device 110 and cause the interface to include a particularnon-linear metric in response to the statistical significance of thenon-linear metric being greater than the user specified significancelevel for that metric.

In some embodiments, as a metric transitions from being non-significantto significant based on a threshold significance level and thestatistical significance of each metric, the interface generator 410causes the metric displayed in the interface to change from a firstcolor to a second color, e.g., from black and white to yellow. When apattern of a metric changes at a higher significance level (which can bedefined based on a user setting or predefined parameters), the interfacegenerator 410 can cause the metrics to become a third color, e.g.,become blue. In some embodiments, the interface generator 410 displays asplit screen of EEG data such that the EEG data is shown in a firstwindow in real time and a period of historical EEG data that has beencategorized as a candidate seizure is also displayed.

When there are no significant changes in the EEG trajectory, there maybe no significance EEG data for review and the interface generator 410can cause the interface to include an indication of no seizure. In someembodiments, the patterns and significance levels of the metrics may beuser defined. In some embodiments, the patterns and/or significancelevels may be predefined.

Referring now to FIG. 5, a process 500 of detecting a candidate seizureby determining changes of non-linear features of an EEG signal is shown,according to an exemplary embodiment. The seizure detector 108 isconfigured to perform the process 500, in some embodiments. Inparticular, the channel selector 402, the filtering stage 404, thepreliminary analyzer 406, and/or the secondary analyzer 408 of theseizure detector 108 are configured to perform some and/or all of theprocess 500, in some embodiments. Furthermore, any computing system ordevice as described herein can be configured to perform the process 500.

In step 502, the channel selector 402 receives EEG data from an array ofEEG electrodes configured to sense brain activity of the patient 102. Insome embodiments, the channel selector 402 receives the EEG datadirectly from the electrodes in real-time, i.e., as the data iscollected. In some embodiments, the channel selector 402 receives thedata after the data has been collected, i.e., from a database or othermemory device storing the EEG data.

In step 504, the channel selector 402 can select an EEG signal of aparticular channel or from multiple channels of the EEG data receive dinthe step 502. Furthermore, the filtering stage 404 can perform filteringon the EEG signal. In some embodiments, the selection of the channelincludes selecting an EEG signal of particular electrode or group ofelectrodes from other EEG signals of other electrodes. In someembodiments, the selection performed by the channel selector 402 ispredefined, i.e., the same channel is always selected. In someembodiments, the channel selection is selected based on configurationdata, i.e., data indicating characteristics of the patient 102, e.g.,age, height, medical syndromes, etc. The filtering may allow aparticular range of frequencies to be passed. In some embodiments, therange of frequencies passed is predetermined. In some embodiments, theranges of frequencies passed is also based on the configuration data.

In step 506, the preliminary analyzer 406 performs a preliminaryanalysis with a generalized metrics suited to detecting a shift in theratio of non-linear versus linear contributors with a moving window. Thepreliminary analyzer 406 can detect trends in the metric. For example,the preliminary analyzer 406 may determine eigenvalues with a movingwindow and determine whether a trajectory of the eigenvalues isincreasing or decreasing. The preliminary analyzer 406 may calculateprobability values for the trajectory based on the values of theeigenvalues. If the probability values become small, i.e., less than apredefined amount, a statistical significance that the eigenvalue isincreasing or decreasing can be identified by the preliminary analyzer406 (e.g., the increase or decrease is statistically significant becausethe probability of the increase or decrease occurring is low).

A decrease in the eigenvalues may indicate a shift in the ratio ofnon-linear and linear contributors, i.e., an increase in the non-linearfeatures of the EEG signal. In response to a detection of an increase inthe non-linear features of the EEG signal in the step 506, the step 508of the process 500 may be performed. If the non-linear features of theEEG signal are not increasing, the step 508 may be skipped so thatcomputational resources are not utilized inefficiently.

In step 508, the secondary analyzer 408 performs a second analysisincluding determining metrics which will more precisely categorize theform of the non-linear change (e.g. changing dimensionality, entropy,degree of separation of the recurrence loops in phase space, Lyapunovexponents, etc.). The metrics analyzed the preliminary analysis (step506) may be computationally efficient while the metrics analyzed in thesecond phase (step 508) may require greater computing resources,accordingly, processing the metrics in separate stages allows for use ofcomputational resources only when necessary, i.e., only after thepreliminary analysis indicates the possibility of a seizure. In the step508, the secondary analyzer 408 can identify trends in the secondmetrics and, based on a particular pattern of changes in the secondmetrics, determine whether the EEG data indicates a candidate seizure orno seizure.

Referring now to FIG. 6, a process 600 of detecting a candidate seizureby determining changes of non-linear features of an EEG signal witheigenvalues, Renyi permutation entropy, and sample entropy is shown,according to an exemplary embodiment. The process 600 provides anexemplary metric analysis that the seizure detector 108 is configured toperform, in some embodiments. The decisions of the process 600 areexemplary, there may be many combinations of metrics and/or analysisrules that can be applied by the seizure detector 108 to detect acandidate seizure. The seizure detector 108 is configured to analyzevarious different patterns with various different non-linear metrics inaddition to, or instead of, eigenvalues, Renyi permutation entropy,and/or sample entropy, in some embodiments. The seizure detector 108 isconfigured to perform the process 600, in some embodiments. Inparticular, the preliminary analyzer 406 and/or the secondary analyzer408 of the seizure detector 108 are configured to perform some and/orall of the process 600, in some embodiments. Furthermore, any computingsystem or device as described herein can be configured to perform theprocess 600.

In step 602, the preliminary analyzer 406 can receive an EEG signal. TheEEG signal may be a signal generated based on electrical brain activityof the patient 102. Furthermore, the EEG signal may be processed by thechannel selector 402 and/or the filtering stage 404 before beingreceived by the preliminary analyzer 406. The EEG signal may be a timeseries of data samples.

In step 604, the preliminary analyzer 406 can determine eigenvaluesbased on the EEG signal. In some embodiments, the preliminary analyzer406 determines the eigenvalues with a moving window of eigenvalues. Forexample, the preliminary analyzer 460 can apply a window with apredefined length and a predefined overlap with a previous location ofthe window to generate an eigenvalue based on samples of the EEG signalfalling within the window.

In step 606, the preliminary analyzer 406 can analyze a trend of theeigenvalues determined in the step 604 to determine whether theeigenvalue is increasing or decreasing over time. In some embodiments,the preliminary analyzer 406 determines a probability of a trajectory,i.e., an overall increase or decrease in the eigenvalues. If theprobability of the trajectory to increase is greater than a predefinedamount, the preliminary analyzer 406 performs the step 608. Similarly,if the probability of the trajectory to decrease is greater than apredefined amount, the preliminary analyzer 406 performs the step 610.If the eigenvalues do not demonstrate a significant increase ordecrease, the preliminary analyzer 406 can classify the EEG data asinsignificant.

If the eigenvalues are increasing, the preliminary analyzer 406 canclassify the EEG data (particular samples of the EEG signal) indicatingthe increase as insignificant in step 608. However, if the preliminaryanalyzer 406 determines that the eigenvalues are decreasing, the data ofthe EEG signal can be classified by the preliminary analyzer 406 assignificant and potentially indicating a candidate seizure, in step 610.

In step 612, the secondary analyzer 408 can determine Renyi permutationentropy values based on the EEG signal. In some embodiments, thesecondary analyzer 408 determines the Renyi permutation entropy valuesfor only segments of EEG data that the preliminary analyzer 406 hasclassified as significant. This may allow the secondary analyzer 408 toonly perform calculations and utilize computational resourcesefficiently.

In step 614, the secondary analyzer 408 can analyze a trend of the Renyipermutation entropy determined in the step 612 to determine whether theRenyi permutation entropy is increasing or decreasing over time. In someembodiments, the secondary analyzer 408 determines a probability of atrajectory, i.e., an overall increase or decrease in the Renyipermutation entropy. If the probability of the trajectory is to increaseis greater than a predefined amount, the secondary analyzer 408 performsthe step 618. Similarly, if the probability of the trajectory is todecrease is greater than a predefined amount, the secondary analyzer 408performs the step 616. If the Renyi permutation entropy does notdemonstrate a significant increase or decrease, the secondary analyzer408 can perform the step 618.

In step 618, the secondary analyzer 408 can determine sample entropyvalues based on the EEG signal. In some embodiments, the secondaryanalyzer 408 determines the sample permutation entropy values for onlysegments of EEG data that the preliminary analyzer 406 has classified assignificant. This may allow the secondary analyzer 408 to only performcalculations, and utilize computational resources, when necessary. Thesample permutation entropy may be determined by the secondary analyzer408 with a moving window, in some embodiments.

In step 620, the secondary analyzer 408 determines whether the sampleentropy is positive or negative. Based on the polarity of the sampleentropy, the secondary analyzer 408 classifies the data as a candidateseizure, the step 624 when the sample entropy is negative, or as noise,the step 622 when the sample entropy is positive.

Configuration of Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements can bereversed or otherwise varied and the nature or number of discreteelements or positions can be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepscan be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions can be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure can be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps canbe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

What is claimed is:
 1. A method of seizure detection, the methodcomprising: receiving, by a processing circuit, an electroencephalogram(EEG) signal generated based on electrical brain activity of a patient;determining, by the processing circuit, a plurality of metrics based onthe EEG signal, the plurality of metrics indicating non-linear featuresof the EEG signal; determining, by the processing circuit, probabilitiesof a trajectory of each of the plurality of metrics at a plurality ofpoints in time; determining, by the processing circuit, whether thetrajectory of each of the plurality of metrics is significant based onthe probabilities; mapping, by the processing circuit, significanttrajectories to a category, wherein the category is a seizure category;and generating, by the processing circuit, a seizure alert indicatingthat the EEG signal indicates a candidate seizure.
 2. The method ofclaim 1, further comprising determining, by the processing circuit, thatthe EEG signal indicates the candidate seizure based on at least one ofa default parameter value or a user defined parameter value.
 3. Themethod of claim 1, wherein the plurality of metrics comprise at leastone of dimensionality, synchrony, Lyapunov exponents, entropy, globalnon-linearity, distance differences between recurrence trajectories, acomplexity metric, or self-similarity.
 4. The method of claim 1, furthercomprising determining, by the processing circuit, that the EEG signalindicates the candidate seizure by determining, based at least in parton the plurality of metrics, a change in the non-linear features of theEEG signal over time by: performing a preliminary analysis with one ofthe plurality of metrics, wherein the preliminary analysis indicatesthat the EEG signal indicates the candidate seizure or that the EEGsignal includes noise; and performing a secondary analysis with one ormore metrics of the plurality of metrics to determine whether the EEGsignal indicates the candidate seizure or that the EEG signal includesthe noise.
 5. The method of claim 1, further comprising determining, bythe processing circuit, a dimensionality of the EEG signal by performinga phase space analysis by increasing a value of the dimensionality untila number of false neighbors reaches zero, wherein a starting value ofthe dimensionality is based on an age of the patient.
 6. The method ofclaim 1, further comprising: receiving, by the processing circuit, theEEG signal from a local EEG acquisition system via a network; andproviding, by the processing circuit, result data to the local EEGacquisition system via the network.
 7. The method of claim 1, whereinthe processing circuit is included within a local seizure detectionsystem, wherein the local seizure detection system is: integrated with alocal EEG system; or connected locally to an EEG acquisition system. 8.The method of claim 1, further comprising: determining, by theprocessing circuit, whether one or more of the plurality of metricsexhibit statistically significant changes over time; generating, by theprocessing circuit, a user interface, the user interface comprising: areal-time trend of the EEG signal; and the one or more of the pluralityof metrics; and causing, by the processing circuit, a user interfacedevice to display the user interface.
 9. The method of claim 8, whereinone of the plurality of metrics is an eigenvalue, wherein the userinterface further comprises a trend of the eigenvalue.
 10. The method ofclaim 8, wherein the user interface further comprises a historicalwindow of the EEG signal, the historical window of the EEG signalassociated with the candidate seizure.
 11. The method of claim 1,further comprising: determining, by the processing circuit, a movingwindow of eigenvalues of the EEG signal; determining, by the processingcircuit, that the eigenvalues are decreasing; and determining, by theprocessing circuit, that the EEG signal indicates the candidate seizurebased at least in part on the plurality of metrics in response to adetermination that the eigenvalues are decreasing.
 12. The method ofclaim 11, further comprising: determining, by the processing circuit,Renyi permutation entropy values based on the EEG signal; determining,by the processing circuit, that the Renyi permutation entropy values aredecreasing; and determining, by the processing circuit, that the EEGsignal indicates the candidate seizure in response to the determinationthat the eigenvalues are decreasing and a second determination that theRenyi permutation entropy values are decreasing.
 13. A method forseizure detection comprising: receiving, by a processing circuit, anelectroencephalogram (EEG) signal from one or more electrodes connectedto a patient, the one or more electrodes configured to sense electricalbrain activity of the patient; determining, by the processing circuit, ametric based on the EEG signal, the metric indicating a non-linearfeature of the EEG signal; determining, by the processing circuit, atrajectory of the non-linear feature by determining a plurality ofvalues of the non-linear feature over time; determining, by theprocessing circuit, that a probability value of the trajectory is lessthan a probability level indicating that an occurrence of the trajectoryis statistically significant; and determining, by the processingcircuit, that the EEG signal indicates a candidate seizure in responseto a determination that the probability value of the trajectory is lessthan the probability level.
 14. The method of claim 13, whereindetermining, by the processing circuit, that the EEG signal indicatesthe candidate seizure based on at least one of a default parameter valueor a user defined parameter value.
 15. The method of claim 13, furthercomprising determining, by the processing circuit, a dimensionality ofthe EEG signal by performing a phase space analysis by increasing avalue of the dimensionality until a number of false neighbors reacheszero, wherein a starting value of the dimensionality is based on an ageof the patient.
 16. The method of claim 13, further comprising:determining, by the processing circuit, a plurality of metricsindicating a plurality of non-linear features of the EEG signal; whereindetermining, by the processing circuit, that the EEG signal indicates acandidate seizure is based on the plurality of metrics; wherein theplurality of metrics comprise at least one of dimensionality, synchrony,Lyapunov exponents, entropy, global non-linearity, distance differencesbetween recurrence trajectories, or self-similarity.
 17. The method ofclaim 13, wherein each value of the plurality of values of thenon-linear feature decreases with respect to a previous value of theplurality of values; wherein determining, by the processing circuit, theprobability value of the trajectory is based on a number of theplurality of values each decreasing with respect to the previous valueof the plurality of values.
 18. The method of claim 13, furthercomprising: receiving, by the processing circuit, user input via a userinterface; and determining, by the processing circuit, the probabilitylevel by setting the probability level to a value selected by a user viathe user input.
 19. The method of claim 13, further comprising:retrieving, by the processing circuit, a default value from a memorydevice; and determining, by the processing circuit, the probabilitylevel by setting the probability level to the default value retrievedfrom the memory device.
 20. The method of claim 13, further comprising:determining, by the processing circuit, Renyi permutation entropy valuesbased on the EEG signal; determining, by the processing circuit, thatthe Renyi permutation entropy values are increasing; determining, by theprocessing circuit, sample entropy values based on the EEG signal inresponse to a first determination that the Renyi permutation entropyvalues are increasing; determining, by the processing circuit, that theEEG signal indicates the candidate seizure in response to a seconddetermination that the sample entropy values are negative; anddetermining, by the processing circuit, that the EEG signal does notindicate the candidate seizure in response to a third determination thatthe sample entropy values are positive.
 21. A method of seizuredetection comprising: receiving, by a processing circuit, anelectroencephalogram (EEG) signal generated based on electrical brainactivity of a patient; determining, by the processing circuit, aplurality of metrics based on the EEG signal, the plurality of metricsindicating non-linear features of the EEG signal; performing, by theprocessing circuit, a preliminary analysis with one of the plurality ofmetrics, wherein the preliminary analysis indicates that the EEG signalindicates a candidate seizure or that the EEG signal includes noise;performing, by the processing circuit, a secondary analysis with one ormore metrics of the plurality of metrics to determine whether the EEGsignal indicates the candidate seizure or that the EEG signal includesthe noise; and generating, by the processing circuit, a seizure alertindicating that the EEG signal indicates the candidate seizure.
 22. Themethod of claim 21, wherein the plurality of metrics comprise at leastone of dimensionality, synchrony, Lyapunov exponents, entropy, globalnon-linearity, distance differences between recurrence trajectories, orself-similarity.
 23. The method of claim 21, further comprisingdetermining, by the processing circuit, a dimensionality of the EEGsignal by performing a phase space analysis by increasing a value of thedimensionality until a number of false neighbors reaches zero, wherein astarting value of the dimensionality is based on an age of the patient.24. The method of claim 21, further comprising determining, by theprocessing circuit, based at least in part of the plurality of metrics,the change in the non-linear features of the EEG signal by determiningan increase in the non-linear features over time.
 25. The method ofclaim 21, further comprising: generating, by the processing circuit, auser interface, the user interface comprising: a real-time trend of theEEG signal; and the one or more of the plurality of metrics; andcausing, by the processing circuit, a user interface device to displaythe user interface.
 26. The method of claim 25, wherein one of theplurality of metrics is an eigenvalue, wherein the user interfacefurther comprises a trend of the eigenvalue.